{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Face Generation\n",
    "\n",
    "In this project, you'll define and train a DCGAN on a dataset of faces. Your goal is to get a generator network to generate *new* images of faces that look as realistic as possible!\n",
    "\n",
    "The project will be broken down into a series of tasks from **loading in data to defining and training adversarial networks**. At the end of the notebook, you'll be able to visualize the results of your trained Generator to see how it performs; your generated samples should look like fairly realistic faces with small amounts of noise.\n",
    "\n",
    "### Get the Data\n",
    "\n",
    "You'll be using the [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) to train your adversarial networks.\n",
    "\n",
    "This dataset is more complex than the number datasets (like MNIST or SVHN) you've been working with, and so, you should prepare to define deeper networks and train them for a longer time to get good results. It is suggested that you utilize a GPU for training.\n",
    "\n",
    "### Pre-processed Data\n",
    "\n",
    "Since the project's main focus is on building the GANs, we've done *some* of the pre-processing for you. Each of the CelebA images has been cropped to remove parts of the image that don't include a face, then resized down to 64x64x3 NumPy images. Some sample data is show below.\n",
    "\n",
    "<img src='assets/processed_face_data.png' width=60% />\n",
    "\n",
    "> If you are working locally, you can download this data [by clicking here](https://s3.amazonaws.com/video.udacity-data.com/topher/2018/November/5be7eb6f_processed-celeba-small/processed-celeba-small.zip)\n",
    "\n",
    "This is a zip file that you'll need to extract in the home directory of this notebook for further loading and processing. After extracting the data, you should be left with a directory of data `processed_celeba_small/`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# can comment out after executing\n",
    "# !unzip processed_celeba_small.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'processed_celeba_small/'\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "import pickle as pkl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import problem_unittests as tests\n",
    "from PIL import Image, ImageFile\n",
    "#import helper\n",
    "\n",
    "%matplotlib inline\n",
    "ImageFile.LOAD_TRUNCATED_IMAGES = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the CelebA Data\n",
    "\n",
    "The [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations, you'll only need the images. Note that these are color images with [3 color channels (RGB)](https://en.wikipedia.org/wiki/Channel_(digital_image)#RGB_Images) each.\n",
    "\n",
    "### Pre-process and Load the Data\n",
    "\n",
    "Since the project's main focus is on building the GANs, we've done *some* of the pre-processing for you. Each of the CelebA images has been cropped to remove parts of the image that don't include a face, then resized down to 64x64x3 NumPy images. This *pre-processed* dataset is a smaller subset of the very large CelebA data.\n",
    "\n",
    "> There are a few other steps that you'll need to **transform** this data and create a **DataLoader**.\n",
    "\n",
    "#### Exercise: Complete the following `get_dataloader` function, such that it satisfies these requirements:\n",
    "\n",
    "* Your images should be square, Tensor images of size `image_size x image_size` in the x and y dimension.\n",
    "* Your function should return a DataLoader that shuffles and batches these Tensor images.\n",
    "\n",
    "#### ImageFolder\n",
    "\n",
    "To create a dataset given a directory of images, it's recommended that you use PyTorch's [ImageFolder](https://pytorch.org/docs/stable/torchvision/datasets.html#imagefolder) wrapper, with a root directory `processed_celeba_small/` and data transformation passed in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# necessary imports\n",
    "import torch\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataloader(batch_size, image_size, data_dir='processed_celeba_small/'):\n",
    "    \"\"\"\n",
    "    Batch the neural network data using DataLoader\n",
    "    :param batch_size: The size of each batch; the number of images in a batch\n",
    "    :param img_size: The square size of the image data (x, y)\n",
    "    :param data_dir: Directory where image data is located\n",
    "    :return: DataLoader with batched data\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: Implement function and return a dataloader\n",
    "    image_aug = transforms.Compose([transforms.Resize(image_size), transforms.CenterCrop(image_size),transforms.ToTensor()])\n",
    "    imagenet_data = datasets.ImageFolder(data_dir, transform=image_aug)\n",
    "    data_loader = torch.utils.data.DataLoader(imagenet_data,\n",
    "                                          batch_size=batch_size + 1,\n",
    "                                          shuffle=True)\n",
    "    \n",
    "    return data_loader\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a DataLoader\n",
    "\n",
    "#### Exercise: Create a DataLoader `celeba_train_loader` with appropriate hyperparameters.\n",
    "\n",
    "Call the above function and create a dataloader to view images. \n",
    "* You can decide on any reasonable `batch_size` parameter\n",
    "* Your `image_size` **must be** `32`. Resizing the data to a smaller size will make for faster training, while still creating convincing images of faces!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function hyperparameters\n",
    "batch_size = 20\n",
    "img_size = 32\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "# Call your function and get a dataloader\n",
    "celeba_train_loader = get_dataloader(batch_size, img_size)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, you can view some images! You should seen square images of somewhat-centered faces.\n",
    "\n",
    "Note: You'll need to convert the Tensor images into a NumPy type and transpose the dimensions to correctly display an image, suggested `imshow` code is below, but it may not be perfect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f725bfbd860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# helper display function\n",
    "def imshow(img):\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "# obtain one batch of training images\n",
    "dataiter = iter(celeba_train_loader)\n",
    "images, _ = dataiter.next() # _ for no labels\n",
    "\n",
    "\n",
    "# plot the images in the batch, along with the corresponding labels\n",
    "fig = plt.figure(figsize=(20, 4))\n",
    "plot_size=20\n",
    "for idx in np.arange(plot_size):\n",
    "    ax = fig.add_subplot(2, plot_size/2, idx+1, xticks=[], yticks=[])\n",
    "    imshow(images[idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Pre-process your image data and scale it to a pixel range of -1 to 1\n",
    "\n",
    "You need to do a bit of pre-processing; you know that the output of a `tanh` activated generator will contain pixel values in a range from -1 to 1, and so, we need to rescale our training images to a range of -1 to 1. (Right now, they are in a range from 0-1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Complete the scale function\n",
    "def scale(x, feature_range=(-1, 1)):\n",
    "    ''' Scale takes in an image x and returns that image, scaled\n",
    "       with a feature_range of pixel values from -1 to 1. \n",
    "       This function assumes that the input x is already scaled from 0-1.'''\n",
    "    # assume x is scaled to (0, 1)\n",
    "    # scale to feature_range and return scaled x\n",
    "    min, max = feature_range\n",
    "    # print(x)\n",
    "    x = x * (max - min) + min\n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(-0.9451)\n",
      "Max:  tensor(0.9765)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "# check scaled range\n",
    "# should be close to -1 to 1\n",
    "img = images[0]\n",
    "scaled_img = scale(img)\n",
    "\n",
    "print('Min: ', scaled_img.min())\n",
    "print('Max: ', scaled_img.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Define the Model\n",
    "\n",
    "A GAN is comprised of two adversarial networks, a discriminator and a generator.\n",
    "\n",
    "## Discriminator\n",
    "\n",
    "Your first task will be to define the discriminator. This is a convolutional classifier like you've built before, only without any maxpooling layers. To deal with this complex data, it's suggested you use a deep network with **normalization**. You are also allowed to create any helper functions that may be useful.\n",
    "\n",
    "#### Exercise: Complete the Discriminator class\n",
    "* The inputs to the discriminator are 32x32x3 tensor images\n",
    "* The output should be a single value that will indicate whether a given image is real or fake\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper conv function\n",
    "def conv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
    "    \"\"\"Creates a convolutional layer, with optional batch normalization.\n",
    "    \"\"\"\n",
    "    layers = []\n",
    "    conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, \n",
    "                           kernel_size=kernel_size, stride=stride, padding=padding, bias=False)\n",
    "    \n",
    "    layers.append(conv_layer)\n",
    "\n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "class Discriminator(nn.Module):\n",
    "\n",
    "    def __init__(self, conv_dim):\n",
    "        \"\"\"\n",
    "        Initialize the Discriminator Module\n",
    "        :param conv_dim: The depth of the first convolutional layer\n",
    "        \"\"\"\n",
    "        super(Discriminator, self).__init__()\n",
    "\n",
    "        # complete init function\n",
    "        self.conv_dim = conv_dim\n",
    "\n",
    "        # Define all convolutional layers\n",
    "        # Should accept an RGB image as input and output a single value\n",
    "        self.conv1 = conv(3, conv_dim, 4, batch_norm=False) # x, y = 64 depth = 3\n",
    "        self.conv2 = conv(conv_dim, conv_dim * 2, 4) # x, y = 32 depth = 64\n",
    "        self.conv3 = conv(conv_dim * 2, conv_dim * 4, 4) # x, y = 16 depth = 128\n",
    "        \n",
    "        self.fc = nn.Linear(conv_dim*4*4*4, 1)\n",
    "        self.out = nn.Sigmoid()\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Forward propagation of the neural network\n",
    "        :param x: The input to the neural network     \n",
    "        :return: Discriminator logits; the output of the neural network\n",
    "        \"\"\"\n",
    "        # define feedforward behavior\n",
    "        x = F.leaky_relu(self.conv1(x), 0.2)\n",
    "        # x = self.dropout(x)\n",
    "        x = F.leaky_relu(self.conv2(x), 0.2)\n",
    "        # x = self.dropout(x)\n",
    "        x = F.leaky_relu(self.conv3(x), 0.2)\n",
    "        # x = self.dropout(x)\n",
    "        \n",
    "        x = x.view(-1, self.conv_dim*4*4*4)\n",
    "        \n",
    "        x = self.fc(x)\n",
    "        x = self.dropout(x)\n",
    "        \n",
    "        # x = self.out(x)\n",
    "        \n",
    "        return x\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_discriminator(Discriminator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator\n",
    "\n",
    "The generator should upsample an input and generate a *new* image of the same size as our training data `32x32x3`. This should be mostly transpose convolutional layers with normalization applied to the outputs.\n",
    "\n",
    "#### Exercise: Complete the Generator class\n",
    "* The inputs to the generator are vectors of some length `z_size`\n",
    "* The output should be a image of shape `32x32x3`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper deconv function\n",
    "def deconv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
    "    \"\"\"Creates a transpose convolutional layer, with optional batch normalization.\n",
    "    \"\"\"\n",
    "    layers = []\n",
    "    # append transpose conv layer\n",
    "    layers.append(nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, bias=False))\n",
    "    # optional batch norm layer\n",
    "    if batch_norm:\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "    return nn.Sequential(*layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "class Generator(nn.Module):\n",
    "    \n",
    "    def __init__(self, z_size, conv_dim):\n",
    "        \"\"\"\n",
    "        Initialize the Generator Module\n",
    "        :param z_size: The length of the input latent vector, z\n",
    "        :param conv_dim: The depth of the inputs to the *last* transpose convolutional layer\n",
    "        \"\"\"\n",
    "        super(Generator, self).__init__()\n",
    "\n",
    "        # complete init function\n",
    "        self.conv_dim = conv_dim\n",
    "        \n",
    "        self.fc = nn.Linear(z_size, conv_dim*4*4*4)\n",
    "        \n",
    "        self.t_conv1 = deconv(conv_dim*4, conv_dim*2, 4 )\n",
    "        self.t_conv2 = deconv(conv_dim*2, conv_dim, 4)\n",
    "        self.t_conv3 = deconv(conv_dim, 3, 4, batch_norm=False)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Forward propagation of the neural network\n",
    "        :param x: The input to the neural network     \n",
    "        :return: A 32x32x3 Tensor image as output\n",
    "        \"\"\"\n",
    "        # define feedforward behavior\n",
    "        \n",
    "        x = self.fc(x)\n",
    "        x = self.dropout(x)\n",
    "        \n",
    "        x = x.view(-1, self.conv_dim*4, 4, 4)\n",
    "        \n",
    "        x = F.relu(self.t_conv1(x))\n",
    "        # x = self.dropout(x)\n",
    "        x = F.relu(self.t_conv2(x))\n",
    "        # x = self.dropout(x)\n",
    "        x = F.tanh(self.t_conv3(x))\n",
    "        \n",
    "        return x\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_generator(Generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize the weights of your networks\n",
    "\n",
    "To help your models converge, you should initialize the weights of the convolutional and linear layers in your model. From reading the [original DCGAN paper](https://arxiv.org/pdf/1511.06434.pdf), they say:\n",
    "> All weights were initialized from a zero-centered Normal distribution with standard deviation 0.02.\n",
    "\n",
    "So, your next task will be to define a weight initialization function that does just this!\n",
    "\n",
    "You can refer back to the lesson on weight initialization or even consult existing model code, such as that from [the `networks.py` file in CycleGAN Github repository](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py) to help you complete this function.\n",
    "\n",
    "#### Exercise: Complete the weight initialization function\n",
    "\n",
    "* This should initialize only **convolutional** and **linear** layers\n",
    "* Initialize the weights to a normal distribution, centered around 0, with a standard deviation of 0.02.\n",
    "* The bias terms, if they exist, may be left alone or set to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.nn import init\n",
    "def weights_init_normal(m):\n",
    "    \"\"\"\n",
    "    Applies initial weights to certain layers in a model .\n",
    "    The weights are taken from a normal distribution \n",
    "    with mean = 0, std dev = 0.02.\n",
    "    :param m: A module or layer in a network    \n",
    "    \"\"\"\n",
    "    init_gain=0.02\n",
    "    # classname will be something like:\n",
    "    # `Conv`, `BatchNorm2d`, `Linear`, etc.\n",
    "    # TODO: Apply initial weights to convolutional and linear layers\n",
    "    classname = m.__class__.__name__\n",
    "    # print(classname)\n",
    "    if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):\n",
    "        init.normal_(m.weight.data, 0.0, init_gain)\n",
    "        if hasattr(m, 'bias') and m.bias is not None:\n",
    "            init.constant_(m.bias.data, 0.0)\n",
    "    elif classname.find('BatchNorm2d') != -1:  # BatchNorm Layer's weight is not a matrix; only normal distribution applies.\n",
    "        init.normal_(m.weight.data, 1.0, init_gain)\n",
    "        init.constant_(m.bias.data, 0.0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Build complete network\n",
    "\n",
    "Define your models' hyperparameters and instantiate the discriminator and generator from the classes defined above. Make sure you've passed in the correct input arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "def build_network(d_conv_dim, g_conv_dim, z_size):\n",
    "    # define discriminator and generator\n",
    "    D = Discriminator(d_conv_dim)\n",
    "    G = Generator(z_size=z_size, conv_dim=g_conv_dim)\n",
    "\n",
    "    # initialize model weights\n",
    "    D.apply(weights_init_normal)\n",
    "    G.apply(weights_init_normal)\n",
    "\n",
    "    print(D)\n",
    "    print()\n",
    "    print(G)\n",
    "    \n",
    "    return D, G\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Define model hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discriminator(\n",
      "  (conv1): Sequential(\n",
      "    (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\n",
      "  (conv2): Sequential(\n",
      "    (0): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (conv3): Sequential(\n",
      "    (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (fc): Linear(in_features=2048, out_features=1, bias=True)\n",
      "  (out): Sigmoid()\n",
      "  (dropout): Dropout(p=0.5)\n",
      ")\n",
      "\n",
      "Generator(\n",
      "  (fc): Linear(in_features=100, out_features=2048, bias=True)\n",
      "  (t_conv1): Sequential(\n",
      "    (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (t_conv2): Sequential(\n",
      "    (0): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (t_conv3): Sequential(\n",
      "    (0): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\n",
      "  (dropout): Dropout(p=0.5)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Define model hyperparams\n",
    "d_conv_dim = 32\n",
    "g_conv_dim = 32\n",
    "z_size = 100\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "D, G = build_network(d_conv_dim, g_conv_dim, z_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training on GPU\n",
    "\n",
    "Check if you can train on GPU. Here, we'll set this as a boolean variable `train_on_gpu`. Later, you'll be responsible for making sure that \n",
    ">* Models,\n",
    "* Model inputs, and\n",
    "* Loss function arguments\n",
    "\n",
    "Are moved to GPU, where appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training on GPU!\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "import torch\n",
    "\n",
    "# Check for a GPU\n",
    "train_on_gpu = torch.cuda.is_available()\n",
    "if not train_on_gpu:\n",
    "    print('No GPU found. Please use a GPU to train your neural network.')\n",
    "else:\n",
    "    print('Training on GPU!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Discriminator and Generator Losses\n",
    "\n",
    "Now we need to calculate the losses for both types of adversarial networks.\n",
    "\n",
    "### Discriminator Losses\n",
    "\n",
    "> * For the discriminator, the total loss is the sum of the losses for real and fake images, `d_loss = d_real_loss + d_fake_loss`. \n",
    "* Remember that we want the discriminator to output 1 for real images and 0 for fake images, so we need to set up the losses to reflect that.\n",
    "\n",
    "\n",
    "### Generator Loss\n",
    "\n",
    "The generator loss will look similar only with flipped labels. The generator's goal is to get the discriminator to *think* its generated images are *real*.\n",
    "\n",
    "#### Exercise: Complete real and fake loss functions\n",
    "\n",
    "**You may choose to use either cross entropy or a least squares error loss to complete the following `real_loss` and `fake_loss` functions.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from random import randrange, uniform\n",
    "\n",
    "def real_loss(D_out):\n",
    "    '''Calculates how close discriminator outputs are to being real.\n",
    "       param, D_out: discriminator logits\n",
    "       return: real loss'''\n",
    "    batch_size = D_out.size(0)\n",
    "    # label smoothing\n",
    "    labels = torch.ones(batch_size) * np.random.uniform(0.7, 1.2)\n",
    "    # labels = torch.ones(batch_size) * 0.9\n",
    "    # labels = torch.ones(batch_size) # real labels = 1\n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    # loss = torch.mean(-loss)\n",
    "    # loss = torch.mean((D_out-1)**2)\n",
    "    return loss\n",
    "\n",
    "def fake_loss(D_out):\n",
    "    '''Calculates how close discriminator outputs are to being fake.\n",
    "       param, D_out: discriminator logits\n",
    "       return: fake loss'''\n",
    "    batch_size = D_out.size(0)\n",
    "    # labels = torch.zeros(batch_size) + 0.0 # fake labels = 0\n",
    "    labels = torch.zeros(batch_size) * np.random.uniform(0.0, 0.3) # fake labels = 0.3\n",
    "    # print(labels)\n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    # loss = torch.mean(loss)\n",
    "    # loss = torch.mean(D_out**2)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7037316225441008"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.uniform(0.7,1.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizers\n",
    "\n",
    "#### Exercise: Define optimizers for your Discriminator (D) and Generator (G)\n",
    "\n",
    "Define optimizers for your models with appropriate hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "# params\n",
    "lr = 0.0005\n",
    "beta1= 0.1\n",
    "beta2= 0.99\n",
    "\n",
    "# Create optimizers for the discriminator D and generator G\n",
    "d_optimizer = optim.Adam(D.parameters(), lr, [beta1, beta2])\n",
    "g_optimizer = optim.Adam(G.parameters(), lr, [beta1, beta2])\n",
    "\n",
    "#d_optimizer = optim.SGD(D.parameters(), lr = lr)\n",
    "# g_optimizer = optim.SGD(G.parameters(), lr = lr, momentum=beta1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "if 0.0005 > 0.001:\n",
    "    print(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Training\n",
    "\n",
    "Training will involve alternating between training the discriminator and the generator. You'll use your functions `real_loss` and `fake_loss` to help you calculate the discriminator losses.\n",
    "\n",
    "* You should train the discriminator by alternating on real and fake images\n",
    "* Then the generator, which tries to trick the discriminator and should have an opposing loss function\n",
    "\n",
    "\n",
    "#### Saving Samples\n",
    "\n",
    "You've been given some code to print out some loss statistics and save some generated \"fake\" samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise: Complete the training function\n",
    "\n",
    "Keep in mind that, if you've moved your models to GPU, you'll also have to move any model inputs to GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(D, G, n_epochs, print_every=50):\n",
    "    '''Trains adversarial networks for some number of epochs\n",
    "       param, D: the discriminator network\n",
    "       param, G: the generator network\n",
    "       param, n_epochs: number of epochs to train for\n",
    "       param, print_every: when to print and record the models' losses\n",
    "       return: D and G losses'''\n",
    "    \n",
    "    # move models to GPU\n",
    "    if train_on_gpu:\n",
    "        D.cuda()\n",
    "        G.cuda()\n",
    "    \n",
    "    # D.train()\n",
    "    # keep track of loss and generated, \"fake\" samples\n",
    "    samples = []\n",
    "    losses = []\n",
    "\n",
    "    # Get some fixed data for sampling. These are images that are held\n",
    "    # constant throughout training, and allow us to inspect the model's performance\n",
    "    sample_size=16\n",
    "    fixed_z = np.random.uniform(-1, 1, size=(sample_size, z_size))\n",
    "    fixed_z = torch.from_numpy(fixed_z).float()\n",
    "    # move z to GPU if available\n",
    "    if train_on_gpu:\n",
    "        fixed_z = fixed_z.cuda()\n",
    "\n",
    "    # epoch training loop\n",
    "    for epoch in range(n_epochs):\n",
    "\n",
    "        # batch training loop\n",
    "        for batch_i, (real_images, _) in enumerate(celeba_train_loader):\n",
    "\n",
    "            batch_size = real_images.size(0)\n",
    "            real_images = scale(real_images)\n",
    "\n",
    "            # ===============================================\n",
    "            #         YOUR CODE HERE: TRAIN THE NETWORKS\n",
    "            # ===============================================\n",
    "            \n",
    "            # 1. Train the discriminator on real and fake images\n",
    "            \n",
    "            d_optimizer.zero_grad()\n",
    "            \n",
    "            if train_on_gpu:\n",
    "                real_images = real_images.cuda()\n",
    "            \n",
    "            d_out = D(real_images)\n",
    "            d_real_loss = real_loss(d_out)\n",
    "            \n",
    "            # Generate fake images\n",
    "            z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
    "            z = torch.from_numpy(z).float()\n",
    "            \n",
    "            if train_on_gpu:\n",
    "                z = z.cuda()\n",
    "                \n",
    "            fake_images = G(z)\n",
    "            \n",
    "            d_fake_out = D(fake_images)\n",
    "            d_fake_loss = fake_loss(d_fake_out)\n",
    "\n",
    "            # add up loss and perform backprop\n",
    "            d_loss = d_real_loss + d_fake_loss\n",
    "            \n",
    "            d_loss.backward()\n",
    "            d_optimizer.step()\n",
    "\n",
    "            # 2. Train the generator with an adversarial loss            \n",
    "            g_optimizer.zero_grad()\n",
    "            \n",
    "            # Generate fake images\n",
    "            z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
    "            z = torch.from_numpy(z).float()\n",
    "            \n",
    "            if train_on_gpu:\n",
    "                z = z.cuda()\n",
    "                \n",
    "            fake_images = G(z)\n",
    "            # D.eval()\n",
    "            # Compute the discriminator losses on fake images \n",
    "            # using flipped labels!\n",
    "            g_fake_out = D(fake_images)\n",
    "            g_loss = real_loss(g_fake_out) # use real loss to flip labels\n",
    "            # g_loss = torch.mean(g_fake_out**2)\n",
    "            # D.train()\n",
    "            # perform backprop\n",
    "            g_loss.backward()\n",
    "            g_optimizer.step()            \n",
    "            \n",
    "            # ===============================================\n",
    "            #              END OF YOUR CODE\n",
    "            # ===============================================\n",
    "\n",
    "            # Print some loss stats\n",
    "            if batch_i % print_every == 0:\n",
    "                # append discriminator loss and generator loss\n",
    "                losses.append((d_loss.item(), g_loss.item()))\n",
    "                # print discriminator and generator loss\n",
    "                print('Epoch [{:5d}/{:5d}] | d_loss: {:6.4f} | g_loss: {:6.4f}'.format(\n",
    "                        epoch+1, n_epochs, d_loss.item(), g_loss.item()))\n",
    "\n",
    "\n",
    "        ## AFTER EACH EPOCH##    \n",
    "        # this code assumes your generator is named G, feel free to change the name\n",
    "        # generate and save sample, fake images\n",
    "        G.eval() # for generating samples\n",
    "        samples_z = G(fixed_z)\n",
    "        samples.append(samples_z)\n",
    "        G.train() # back to training mode\n",
    "        print('---------------Epoch [{:5d}/{:5d}]---------------'.format(epoch+1, n_epochs))\n",
    "    # Save training generator samples\n",
    "    with open('train_samples.pkl', 'wb') as f:\n",
    "        pkl.dump(samples, f)\n",
    "    \n",
    "    # finally return losses\n",
    "    return losses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set your number of training epochs and train your GAN!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    1/    2] | d_loss: 1.4754 | g_loss: 0.6976\n",
      "Epoch [    1/    2] | d_loss: 1.3522 | g_loss: 0.8297\n",
      "Epoch [    1/    2] | d_loss: 1.4047 | g_loss: 0.8481\n",
      "Epoch [    1/    2] | d_loss: 1.3158 | g_loss: 0.5097\n",
      "Epoch [    1/    2] | d_loss: 1.7424 | g_loss: 0.6431\n",
      "Epoch [    1/    2] | d_loss: 1.5556 | g_loss: 0.9022\n",
      "Epoch [    1/    2] | d_loss: 1.5950 | g_loss: 0.8906\n",
      "Epoch [    1/    2] | d_loss: 1.3627 | g_loss: 0.7829\n",
      "Epoch [    1/    2] | d_loss: 1.1694 | g_loss: 1.3405\n",
      "Epoch [    1/    2] | d_loss: 0.9383 | g_loss: 1.1203\n",
      "Epoch [    1/    2] | d_loss: 1.0895 | g_loss: 0.9561\n",
      "Epoch [    1/    2] | d_loss: 1.3728 | g_loss: 1.5281\n",
      "Epoch [    1/    2] | d_loss: 1.2485 | g_loss: 0.6826\n",
      "Epoch [    1/    2] | d_loss: 1.4912 | g_loss: 0.9221\n",
      "Epoch [    1/    2] | d_loss: 0.8763 | g_loss: 0.7848\n",
      "Epoch [    1/    2] | d_loss: 1.1869 | g_loss: 1.2932\n",
      "Epoch [    1/    2] | d_loss: 1.5512 | g_loss: 2.8038\n",
      "Epoch [    1/    2] | d_loss: 1.5831 | g_loss: 0.9425\n",
      "Epoch [    1/    2] | d_loss: 1.2707 | g_loss: 0.7488\n",
      "Epoch [    1/    2] | d_loss: 1.4318 | g_loss: 0.5374\n",
      "Epoch [    1/    2] | d_loss: 1.2902 | g_loss: 1.5935\n",
      "Epoch [    1/    2] | d_loss: 1.3016 | g_loss: 0.7961\n",
      "Epoch [    1/    2] | d_loss: 1.5434 | g_loss: 0.8475\n",
      "Epoch [    1/    2] | d_loss: 1.4583 | g_loss: 1.5864\n",
      "Epoch [    1/    2] | d_loss: 1.2222 | g_loss: 0.8156\n",
      "Epoch [    1/    2] | d_loss: 1.4479 | g_loss: 1.3194\n",
      "Epoch [    1/    2] | d_loss: 1.3943 | g_loss: 1.3181\n",
      "Epoch [    1/    2] | d_loss: 1.1768 | g_loss: 1.9713\n",
      "Epoch [    1/    2] | d_loss: 1.4361 | g_loss: 0.6637\n",
      "Epoch [    1/    2] | d_loss: 1.1504 | g_loss: 1.0587\n",
      "Epoch [    1/    2] | d_loss: 1.3328 | g_loss: 1.6088\n",
      "Epoch [    1/    2] | d_loss: 1.8314 | g_loss: 0.8342\n",
      "Epoch [    1/    2] | d_loss: 1.4832 | g_loss: 1.7294\n",
      "Epoch [    1/    2] | d_loss: 1.2785 | g_loss: 1.2184\n",
      "Epoch [    1/    2] | d_loss: 1.2155 | g_loss: 1.5200\n",
      "Epoch [    1/    2] | d_loss: 1.4803 | g_loss: 1.4881\n",
      "Epoch [    1/    2] | d_loss: 1.2564 | g_loss: 1.0053\n",
      "Epoch [    1/    2] | d_loss: 1.1781 | g_loss: 1.2762\n",
      "Epoch [    1/    2] | d_loss: 1.2142 | g_loss: 1.5626\n",
      "Epoch [    1/    2] | d_loss: 1.1612 | g_loss: 1.2105\n",
      "Epoch [    1/    2] | d_loss: 1.3606 | g_loss: 0.9025\n",
      "Epoch [    1/    2] | d_loss: 1.3848 | g_loss: 1.0202\n",
      "Epoch [    1/    2] | d_loss: 1.0529 | g_loss: 0.9017\n",
      "Epoch [    1/    2] | d_loss: 1.4278 | g_loss: 0.7139\n",
      "Epoch [    1/    2] | d_loss: 1.3656 | g_loss: 0.9358\n",
      "Epoch [    1/    2] | d_loss: 1.3355 | g_loss: 0.9468\n",
      "Epoch [    1/    2] | d_loss: 1.2361 | g_loss: 0.8173\n",
      "Epoch [    1/    2] | d_loss: 1.3863 | g_loss: 1.2149\n",
      "Epoch [    1/    2] | d_loss: 0.9947 | g_loss: 1.3497\n",
      "Epoch [    1/    2] | d_loss: 1.3884 | g_loss: 0.8209\n",
      "Epoch [    1/    2] | d_loss: 1.3158 | g_loss: 0.9173\n",
      "Epoch [    1/    2] | d_loss: 1.1591 | g_loss: 1.1379\n",
      "Epoch [    1/    2] | d_loss: 1.1345 | g_loss: 0.8430\n",
      "Epoch [    1/    2] | d_loss: 0.8410 | g_loss: 1.0584\n",
      "Epoch [    1/    2] | d_loss: 1.7511 | g_loss: 1.3362\n",
      "Epoch [    1/    2] | d_loss: 1.7285 | g_loss: 0.7758\n",
      "Epoch [    1/    2] | d_loss: 1.6053 | g_loss: 1.4073\n",
      "Epoch [    1/    2] | d_loss: 1.2497 | g_loss: 0.8182\n",
      "Epoch [    1/    2] | d_loss: 1.2124 | g_loss: 1.3339\n",
      "Epoch [    1/    2] | d_loss: 1.6634 | g_loss: 1.8871\n",
      "Epoch [    1/    2] | d_loss: 1.2393 | g_loss: 0.6714\n",
      "Epoch [    1/    2] | d_loss: 0.9856 | g_loss: 1.0007\n",
      "Epoch [    1/    2] | d_loss: 1.8010 | g_loss: 0.8058\n",
      "Epoch [    1/    2] | d_loss: 0.9025 | g_loss: 1.2714\n",
      "Epoch [    1/    2] | d_loss: 0.9510 | g_loss: 0.7678\n",
      "Epoch [    1/    2] | d_loss: 1.3368 | g_loss: 0.8944\n",
      "Epoch [    1/    2] | d_loss: 1.3415 | g_loss: 0.4086\n",
      "Epoch [    1/    2] | d_loss: 1.2163 | g_loss: 0.8790\n",
      "Epoch [    1/    2] | d_loss: 0.9299 | g_loss: 1.1076\n",
      "Epoch [    1/    2] | d_loss: 1.2523 | g_loss: 1.4197\n",
      "Epoch [    1/    2] | d_loss: 1.8451 | g_loss: 1.8239\n",
      "Epoch [    1/    2] | d_loss: 1.2395 | g_loss: 1.2549\n",
      "Epoch [    1/    2] | d_loss: 1.4959 | g_loss: 1.7491\n",
      "Epoch [    1/    2] | d_loss: 1.1852 | g_loss: 0.7961\n",
      "Epoch [    1/    2] | d_loss: 1.3878 | g_loss: 1.1535\n",
      "Epoch [    1/    2] | d_loss: 1.5561 | g_loss: 0.7455\n",
      "Epoch [    1/    2] | d_loss: 1.3283 | g_loss: 1.9688\n",
      "Epoch [    1/    2] | d_loss: 1.3925 | g_loss: 0.8972\n",
      "Epoch [    1/    2] | d_loss: 1.1807 | g_loss: 0.8900\n",
      "Epoch [    1/    2] | d_loss: 1.3106 | g_loss: 1.6983\n",
      "Epoch [    1/    2] | d_loss: 1.4267 | g_loss: 1.3909\n",
      "Epoch [    1/    2] | d_loss: 1.3119 | g_loss: 0.8439\n",
      "Epoch [    1/    2] | d_loss: 1.0524 | g_loss: 0.8449\n",
      "Epoch [    1/    2] | d_loss: 1.2366 | g_loss: 1.3562\n",
      "Epoch [    1/    2] | d_loss: 1.7296 | g_loss: 0.7194\n",
      "Epoch [    1/    2] | d_loss: 1.0488 | g_loss: 0.7755\n",
      "---------------Epoch [    1/    2]---------------\n",
      "Epoch [    2/    2] | d_loss: 1.1805 | g_loss: 1.1612\n",
      "Epoch [    2/    2] | d_loss: 1.3450 | g_loss: 1.0327\n",
      "Epoch [    2/    2] | d_loss: 1.0701 | g_loss: 0.4351\n",
      "Epoch [    2/    2] | d_loss: 1.0897 | g_loss: 0.7389\n",
      "Epoch [    2/    2] | d_loss: 1.1894 | g_loss: 1.2204\n",
      "Epoch [    2/    2] | d_loss: 1.5936 | g_loss: 1.2484\n",
      "Epoch [    2/    2] | d_loss: 1.2591 | g_loss: 1.4903\n",
      "Epoch [    2/    2] | d_loss: 0.9309 | g_loss: 0.8955\n",
      "Epoch [    2/    2] | d_loss: 1.2292 | g_loss: 1.8356\n",
      "Epoch [    2/    2] | d_loss: 1.7054 | g_loss: 1.6284\n",
      "Epoch [    2/    2] | d_loss: 1.3684 | g_loss: 1.2450\n",
      "Epoch [    2/    2] | d_loss: 1.3733 | g_loss: 0.7795\n",
      "Epoch [    2/    2] | d_loss: 1.4633 | g_loss: 1.2066\n",
      "Epoch [    2/    2] | d_loss: 1.1775 | g_loss: 1.1366\n",
      "Epoch [    2/    2] | d_loss: 1.4024 | g_loss: 1.7606\n",
      "Epoch [    2/    2] | d_loss: 1.4239 | g_loss: 0.9841\n",
      "Epoch [    2/    2] | d_loss: 1.0566 | g_loss: 0.8150\n",
      "Epoch [    2/    2] | d_loss: 2.3074 | g_loss: 1.5561\n",
      "Epoch [    2/    2] | d_loss: 0.9763 | g_loss: 1.1386\n",
      "Epoch [    2/    2] | d_loss: 1.2683 | g_loss: 1.0613\n",
      "Epoch [    2/    2] | d_loss: 1.4429 | g_loss: 1.2129\n",
      "Epoch [    2/    2] | d_loss: 1.2478 | g_loss: 0.7494\n",
      "Epoch [    2/    2] | d_loss: 1.2929 | g_loss: 0.7045\n",
      "Epoch [    2/    2] | d_loss: 1.0548 | g_loss: 1.1101\n",
      "Epoch [    2/    2] | d_loss: 1.2747 | g_loss: 1.2441\n",
      "Epoch [    2/    2] | d_loss: 1.1675 | g_loss: 0.8492\n",
      "Epoch [    2/    2] | d_loss: 1.2712 | g_loss: 0.8854\n",
      "Epoch [    2/    2] | d_loss: 1.3342 | g_loss: 1.2260\n",
      "Epoch [    2/    2] | d_loss: 1.2718 | g_loss: 0.9517\n",
      "Epoch [    2/    2] | d_loss: 1.4739 | g_loss: 1.1627\n",
      "Epoch [    2/    2] | d_loss: 1.0394 | g_loss: 1.0889\n",
      "Epoch [    2/    2] | d_loss: 2.2826 | g_loss: 1.6677\n",
      "Epoch [    2/    2] | d_loss: 1.4866 | g_loss: 1.2877\n",
      "Epoch [    2/    2] | d_loss: 1.0333 | g_loss: 0.5702\n",
      "Epoch [    2/    2] | d_loss: 1.5485 | g_loss: 1.6314\n",
      "Epoch [    2/    2] | d_loss: 1.1268 | g_loss: 0.6506\n",
      "Epoch [    2/    2] | d_loss: 1.3418 | g_loss: 1.3621\n",
      "Epoch [    2/    2] | d_loss: 1.4185 | g_loss: 1.1141\n",
      "Epoch [    2/    2] | d_loss: 1.4481 | g_loss: 0.7957\n",
      "Epoch [    2/    2] | d_loss: 1.2192 | g_loss: 0.8617\n",
      "Epoch [    2/    2] | d_loss: 1.2405 | g_loss: 0.8527\n",
      "Epoch [    2/    2] | d_loss: 1.1804 | g_loss: 0.8142\n",
      "Epoch [    2/    2] | d_loss: 1.3648 | g_loss: 1.1161\n",
      "Epoch [    2/    2] | d_loss: 0.9893 | g_loss: 0.8650\n",
      "Epoch [    2/    2] | d_loss: 1.8677 | g_loss: 4.1073\n",
      "Epoch [    2/    2] | d_loss: 1.4065 | g_loss: 0.9073\n",
      "Epoch [    2/    2] | d_loss: 1.3869 | g_loss: 0.6613\n",
      "Epoch [    2/    2] | d_loss: 1.1605 | g_loss: 1.2273\n",
      "Epoch [    2/    2] | d_loss: 1.4496 | g_loss: 0.8884\n",
      "Epoch [    2/    2] | d_loss: 1.2280 | g_loss: 1.2610\n",
      "Epoch [    2/    2] | d_loss: 1.2091 | g_loss: 0.9232\n",
      "Epoch [    2/    2] | d_loss: 1.3250 | g_loss: 1.4197\n",
      "Epoch [    2/    2] | d_loss: 1.2263 | g_loss: 1.0109\n",
      "Epoch [    2/    2] | d_loss: 1.3736 | g_loss: 2.2814\n",
      "Epoch [    2/    2] | d_loss: 1.0288 | g_loss: 1.3732\n",
      "Epoch [    2/    2] | d_loss: 1.5657 | g_loss: 1.1993\n",
      "Epoch [    2/    2] | d_loss: 1.0549 | g_loss: 0.7359\n",
      "Epoch [    2/    2] | d_loss: 1.8699 | g_loss: 2.1957\n",
      "Epoch [    2/    2] | d_loss: 2.1092 | g_loss: 0.6859\n",
      "Epoch [    2/    2] | d_loss: 1.3586 | g_loss: 0.6875\n",
      "Epoch [    2/    2] | d_loss: 1.4338 | g_loss: 0.8683\n",
      "Epoch [    2/    2] | d_loss: 1.2475 | g_loss: 0.7938\n",
      "Epoch [    2/    2] | d_loss: 1.7584 | g_loss: 0.6860\n",
      "Epoch [    2/    2] | d_loss: 1.4118 | g_loss: 1.9330\n",
      "Epoch [    2/    2] | d_loss: 1.7244 | g_loss: 1.4109\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    2/    2] | d_loss: 1.0947 | g_loss: 1.6847\n",
      "Epoch [    2/    2] | d_loss: 1.2320 | g_loss: 1.3045\n",
      "Epoch [    2/    2] | d_loss: 1.3485 | g_loss: 2.2325\n",
      "Epoch [    2/    2] | d_loss: 1.4554 | g_loss: 0.8019\n",
      "Epoch [    2/    2] | d_loss: 1.0686 | g_loss: 1.0497\n",
      "Epoch [    2/    2] | d_loss: 1.3968 | g_loss: 0.8411\n",
      "Epoch [    2/    2] | d_loss: 1.4112 | g_loss: 1.2163\n",
      "Epoch [    2/    2] | d_loss: 1.2203 | g_loss: 1.4077\n",
      "Epoch [    2/    2] | d_loss: 1.0430 | g_loss: 0.9134\n",
      "Epoch [    2/    2] | d_loss: 1.5914 | g_loss: 1.5384\n",
      "Epoch [    2/    2] | d_loss: 1.4387 | g_loss: 1.5640\n",
      "Epoch [    2/    2] | d_loss: 0.8682 | g_loss: 0.6215\n",
      "Epoch [    2/    2] | d_loss: 1.3420 | g_loss: 1.0239\n",
      "Epoch [    2/    2] | d_loss: 1.5783 | g_loss: 0.5539\n",
      "Epoch [    2/    2] | d_loss: 1.1857 | g_loss: 1.1901\n",
      "Epoch [    2/    2] | d_loss: 1.3195 | g_loss: 0.9429\n",
      "Epoch [    2/    2] | d_loss: 1.3497 | g_loss: 0.9066\n",
      "Epoch [    2/    2] | d_loss: 0.7442 | g_loss: 0.6936\n",
      "Epoch [    2/    2] | d_loss: 1.2338 | g_loss: 0.9582\n",
      "Epoch [    2/    2] | d_loss: 1.1772 | g_loss: 0.8543\n",
      "Epoch [    2/    2] | d_loss: 1.1279 | g_loss: 0.6834\n",
      "---------------Epoch [    2/    2]---------------\n"
     ]
    }
   ],
   "source": [
    "# set number of epochs \n",
    "n_epochs = 2\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "# call training function\n",
    "losses = train(D, G, n_epochs=n_epochs, print_every=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Training loss\n",
    "\n",
    "Plot the training losses for the generator and discriminator, recorded after each epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f725a6c1470>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7236f7c908>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "losses = np.array(losses)\n",
    "plt.plot(losses.T[0], label='Discriminator', alpha=0.5)\n",
    "plt.plot(losses.T[1], label='Generator', alpha=0.5)\n",
    "plt.title(\"Training Losses\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above Result is for hyperparameter <br/> d_conv_dim = 64 <br/>\n",
    "g_conv_dim = 64 <br/>\n",
    "z_size = 100 <br/>\n",
    "epochs = 16 <br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"&#10;\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above Result is for hyperparameter <br/>\n",
    "d_conv_dim = 128 <br/>\n",
    "g_conv_dim = 128 <br/>\n",
    "z_size = 100 <br/>\n",
    "epochs = 20 <br/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Generator samples from training\n",
    "\n",
    "View samples of images from the generator, and answer a question about the strengths and weaknesses of your trained models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper function for viewing a list of passed in sample images\n",
    "def view_samples(epoch, samples):\n",
    "    fig, axes = plt.subplots(figsize=(16,4), nrows=2, ncols=8, sharey=True, sharex=True)\n",
    "    for ax, img in zip(axes.flatten(), samples[epoch]):\n",
    "        img = img.detach().cpu().numpy()\n",
    "        img = np.transpose(img, (1, 2, 0))\n",
    "        img = ((img + 1)*255 / (2)).astype(np.uint8)\n",
    "        ax.xaxis.set_visible(False)\n",
    "        ax.yaxis.set_visible(False)\n",
    "        im = ax.imshow(img.reshape((32,32,3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load samples from generator, taken while training\n",
    "with open('train_samples.pkl', 'rb') as f:\n",
    "    samples = pkl.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f725beafef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = view_samples(-1, samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question: What do you notice about your generated samples and how might you improve this model?\n",
    "When you answer this question, consider the following factors:\n",
    "* The dataset is biased; it is made of \"celebrity\" faces that are mostly white\n",
    "* Model size; larger models have the opportunity to learn more features in a data feature space\n",
    "* Optimization strategy; optimizers and number of epochs affect your final result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** (Write your answer in this cell)\n",
    "\n",
    "What do you notice about your generated samples and how might you improve this model?\n",
    "When you answer this question, consider the following factors:\n",
    "\n",
    "The dataset is biased; it is made of \"celebrity\" faces that are mostly white\n",
    "Model size; larger models have the opportunity to learn more features in a data feature space\n",
    "Optimization strategy; optimizers and number of epochs affect your final result\n",
    "\n",
    "I trained the model for 20 epochs nearly 1 hr of training with different optimizer and network size.\n",
    "an i got learning rate value from original DCGAN paper.\n",
    "\n",
    "learning rate = 0.0002\n",
    "\n",
    "- trial 1\n",
    "\n",
    "d_conv_dim = 32\n",
    "g_conv_dim = 32\n",
    "z_size = 100\n",
    "epochs = 20\n",
    "\n",
    "discriminator loss starts from 1.5 and slowly reduced to 0.5 \n",
    "generator loss starts from 2.0 and slowly decreases up to 5 epochs and the loss start increasing 3.5 after 20 epochs\n",
    "\n",
    "- trial 2\n",
    "\n",
    "d_conv_dim = 64\n",
    "g_conv_dim = 64\n",
    "z_size = 100\n",
    "epochs = 16\n",
    "\n",
    "discriminator loss starts from 1.5 and slowly reduced to 0.73 at 16th epoch\n",
    "generator loss starts from 4.4 and slowly decreases up to 10 epochs to 0.50 and the loss goes zig-zag like sinusoidal between 2.2 to 1.3\n",
    "\n",
    "- trial 2\n",
    "\n",
    "d_conv_dim = 128\n",
    "g_conv_dim = 128\n",
    "z_size = 100\n",
    "epochs = 20\n",
    "\n",
    "discriminator loss starts from 1.7 and slowly reduced to 0.62 at 20th epochs\n",
    "generator loss starts from 5.4 and slowly decreases to 0.3 up to 16 epochs and the loss goes zig-zag like sinusoidal between 2.9 to 1.1\n",
    "\n",
    "- trial 3\n",
    "\n",
    "i changed the optimizer to SGD with momentum 0.5\n",
    "d_conv_dim = 32\n",
    "g_conv_dim = 32\n",
    "z_size = 100\n",
    "epochs = 10\n",
    "\n",
    "discriminator loss and generator loss starts increasing 1.0 to 22.4224 and at end no image is generated. So i drop this.\n",
    "\n",
    "So i think increasing the Conv dimension and reducing the epochs can produce the best result. even increasing the image size to 64 can produce more accurate image with little clarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submitting This Project\n",
    "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_face_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"problem_unittests.py\" files in your submission."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
